Simulation of snow accumulation and melt in needleleaf forest environments
Bibliographic record
Abstract
Abstract. Drawing upon numerous field studies and modelling exercises of snow processes, the Cold Regions Hydrological Model (CRHM) was developed to simulate the four season hydrological cycle in cold regions. CRHM includes modules describing radiative, turbulent and conductive energy exchanges to snow in open and forest environments, as well as account for losses from canopy snow sublimation and rain evaporation. Due to the physical-basis and rigorous testing of each module, there is a minimal need for model calibration. To evaluate CRHM, simulations of snow accumulation and melt were compared to observations collected at paired forest and clearing sites of varying latitude, elevation, forest cover density, and climate. Overall, results show that CRHM is capable of characterising the variation in snow accumulation between forest and clearing sites, achieving a model efficiency of 0.51 for simulations at individual sites. Simulations of canopy sublimation losses slightly overestimated observed losses from a weighed cut tree, having a model efficiency of 0.41 for daily losses. Good model performance was demonstrated in simulating energy fluxes to snow at the clearings, but results were degraded from this under forest cover due to errors in simulating sub-canopy net longwave radiation. However, expressed as cumulative energy to snow over the winter, simulated values were 96% and 98% of that observed at the forest and clearing sites, respectively. Overall, the good representation of the substantial variations in mass and energy between forest and clearing sites suggests that CRHM may be useful as an analytical or predictive tool for snow processes in needleleaf forest environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".